On-line analytical processing (OLAP) refers to consolidating, viewing, and analyzing data in the manner of “multi-dimensional data analysis.” In OLAP systems, data can be aggregated, summarized, consolidated, summed, viewed, and analyzed. OLAP generally comprises numerous, speculative “what-if” and/or “why” data model scenarios executed by a computer. Within these scenarios, the values of key variables or parameters are changed, often repeatedly, to reflect potential variances in measured data. Additional data is then synthesized through animation of the data model. This often includes the consolidation of projected and actual data according to more than one consolidation path or dimension.
Data consolidation is the process of synthesizing data into essential knowledge. The highest level in a data consolidation path is referred to as that data's dimension. A given data dimension represents a specific perspective of the data included in its associated consolidation path. There are typically a number of different dimensions from which a given pool of data can be analyzed. This plural perspective, or Multi-Dimensional Conceptual View, appears to be the way most business persons naturally view their enterprise. Each of these perspectives is considered to be a complementary data dimension. Simultaneous analysis of multiple data dimensions is referred to as multi-dimensional data analysis.
OLAP functionality is characterized by dynamic multi-dimensional analysis of consolidated data supporting end user analytical and navigational activities including:                calculations and modeling applied across dimensions, through hierarchies and/or across members;        trend analysis over sequential time periods;        slicing subsets for on-screen viewing;        drill-down to deeper levels of consolidation;        reach-through to underlying detail data; and        rotation to new dimensional comparisons in the viewing area.        
OLAP is often implemented in a multi-user client/server mode and attempts to offer consistently rapid response to database access, regardless of database size and complexity.
Multi-dimensional databases provide a means for business analysts to easily view summary data and other derived data in a multi-dimensional model of a business. Such a model can be used to test whether a particular hypothesis about the operation of the business is true or not. However, such models can be very large and so it can be difficult to “see” where the most interesting “features” are in a vast numeric landscape comprising millions, or even billions of values. That is, a multi-dimensional OLAP system has multiple dimensions and members within the dimensions. It is typically difficult and time-consuming to locate particular data within the multi-dimensional OLAP system.
One conventional system is described in U.S. Pat. No. 5,359,724 (hereinafter the '724 patent), issued on Oct. 25, 1994 to Robert J. Earle, and entitled “Method and Apparatus for Storing and Retrieving Multi-Dimensional Data in Computer Memory”. Multi-dimensional data is organized as sparse and dense dimensions in a two level structure. In particular, the dense dimensions form a block of data having cells, with each cell holding a value for a combination of sparse dimensions. This technique requires a user to specify a combination of sparse dimensions to access the multi-dimensional data. This places a burden on the user to know the sparse dimensions and the combination required to access a value in a cell. It also is time consuming for a user to use this technique to access data in many cells.
Sunita Sarawagi in “Indexing OLAP Data”, Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 1996, prototyped a system for coloring cells in a Microsoft® Excel pivot table and devised a scheme to lead an analyst from high-level cells to lower-level cells of interest, however, no mechanism for integrating this technology with multi-dimensional databases was devised. Furthermore, the navigation process described was tedious, particularly in large cubes, and required the user to navigate to each cell and view the feature subjectively.
Therefore, there is a need in the art for an improved technique for navigating to desired data in a multi-dimensional database.